Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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Tri Bird Technique for Effective Face Recognition Using Deep Convolutional Neural Network

Authors
Jitendra Chandrakant Musale1, *, Anuj Kumar Singh2, Swati Shirke3, Rahul Rathod4
1CSE, Shri Jagdishprasad Jhabarmal Tibrewala University, Rajasthan, India
2CSE, School of Computing Science and Engineering, Galgotias University, Greater Noida, India
3CSE, MIT ADT University, Pune, Maharashtra, India
4CE, Dr. Babasaheb Ambedkar Technological University, Dist - Raigad, Maharashtra, Lonere, India
*Corresponding author. Email: jitendramusalephd@gmail.com
Corresponding Author
Jitendra Chandrakant Musale
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_33How to use a DOI?
Keywords
Face recognition; Face detection; Ensemble model; Frame selection; Tri-Bird optimization; AlexNet101
Abstract

The massive amount of data produced from Video content, has speedily grown up with surveillance data. Human beings can easily handle this data by greatly outperforming the capacity of technical and non-technical resources. In the current era of computer vision great characteristics like precision, effectiveness and interoperability of smart surveillance systems to retrieve data. Security surveillance systems rapidly increase Face value acknowledgement over different subsequent critical methods. In proposed research, deep CNN and deep BiLSTM methods were used for effective face recognition from video input. Generally, the said techniques efficiently handle challenges face recognition by combining the recompenses of computer vision and tri bird optimization. The proposed method can be used in many computer vision applications that help recognize faces. The hybrid deep model is developed using Distributed Deep CNN classifier and distributed BiLSTM classifier, which is optimized using the new optimization that is designed newly for your work. The new optimization is designed based on the characters of vulture, sparrow and crow. Then the output of the classifier is fed through the model by analyzing the face query to attain the retrieved relevant face. The outcome can be measured by enactment over the proposed supposed model and is analyzed through considering metrics such as accuracy, exactness, remembrance and F-value calculator which are implemented in the MATLAB tool to reveal the proposed face recognition method. The proposed model achieves 91.30% accuracy, 95.07% precision, 95.078% recall and 95.078% f-measures by deep convolutional neural network.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
978-94-6463-314-6
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_33How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jitendra Chandrakant Musale
AU  - Anuj Kumar Singh
AU  - Swati Shirke
AU  - Rahul Rathod
PY  - 2023
DA  - 2023/12/21
TI  - Tri Bird Technique for Effective Face Recognition Using Deep Convolutional Neural Network
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 334
EP  - 347
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_33
DO  - 10.2991/978-94-6463-314-6_33
ID  - Musale2023
ER  -